Update app.py

app.py

@@ -1,3 +1,119 @@
+# import streamlit as st
+# import tensorflow as tf
+# from tensorflow.keras.preprocessing import image
+# import numpy as np
+# from PIL import Image
+# import base64
+
+# hide_streamlit_style = """
+#                 <style>
+#                 div[data-testid="stToolbar"] {
+#                 visibility: hidden;
+#                 height: 0%;
+#                 position: fixed;
+#                 }
+#                 div[data-testid="stDecoration"] {
+#                 visibility: hidden;
+#                 height: 0%;
+#                 position: fixed;
+#                 }
+#                 div[data-testid="stStatusWidget"] {
+#                 visibility: hidden;
+#                 height: 0%;
+#                 position: fixed;
+#                 }
+#                 #MainMenu {
+#                 visibility: hidden;
+#                 height: 0%;
+#                 }
+#                 header {
+#                 visibility: hidden;
+#                 height: 0%;
+#                 }
+#                 footer {
+#                 visibility: hidden;
+#                 height: 0%;
+#                 }
+#                 </style>
+#                 """
+# st.markdown(hide_streamlit_style, unsafe_allow_html=True)
+
+
+# # Load the pre-trained model
+# model = tf.keras.models.load_model('model.h5') 
+# # Define the target size for the model
+# img_size = (224, 224)
+
+# # Function to preprocess the image
+# def preprocess_image(img):
+#     img = image.load_img(img, target_size=img_size)
+#     img_array = image.img_to_array(img)
+#     img_array = img_array / 255.0  # Normalize pixel values to between 0 and 1
+#     img_array = np.expand_dims(img_array, axis=0)
+#     return img_array
+
+# # Function to make predictions
+# def predict_image(img):
+#     img_array = preprocess_image(img)
+#     prediction = model.predict(img_array)
+#     prediction = np.squeeze(prediction, axis=0)
+#     return prediction
+
+# # Function to display and provide a download link for an image
+# def display_image_with_download(image_path, caption, download_text):
+#     image = Image.open(image_path)
+#     st.image(image, caption=caption, use_column_width=True)
+    
+#     # Generate a download link
+#     with open(image_path, 'rb') as f:
+#         data = f.read()
+#         base64_data = base64.b64encode(data).decode('utf-8')
+#         href = f'<a href="data:application/octet-stream;base64,{base64_data}" download="{download_text}.jpg">Download {download_text}</a>'
+#         st.markdown(href, unsafe_allow_html=True)
+
+# # Streamlit app
+# def main():
+        
+#     st.title("Pneumonia Detection")
+
+#     # Allow user to upload an image
+#     uploaded_file = st.file_uploader("Upload a chest X-ray image in JPG format...", type="jpg")
+
+#     # Example instructions
+#     st.markdown("""
+#         Example Instructions:
+#         - Upload a chest X-ray image in JPG format.
+#         - Or, download sample images below and check the predictions.
+#     """)
+
+#    # Provide links to download sample images
+#     st.write("**Download Sample Images:**")
+    
+#     pneumonic_download = st.button("Download Pneumonic Image")
+#     normal_download = st.button("Download Normal Image")
+
+#     if pneumonic_download:
+#         pneumonic_image_path = "test-pneumonia_028.jpg"  # Replace with actual path
+#         display_image_with_download(pneumonic_image_path, "Pneumonic Image", "Pneumonic Image")
+
+#     if normal_download:
+#         normal_image_path = "test-normal_001.jpg"  # Replace with actual path
+#         display_image_with_download(normal_image_path, "Normal Image", "Normal Image")
+
+#     if uploaded_file is not None:
+#         st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
+
+#         # Make predictions
+#         prediction = predict_image(uploaded_file)
+
+#         # Display the results
+#         st.write("**Prediction:**")
+#         class_label = "Pneumonia" if prediction > 0.5 else "Normal"
+#         st.write(f"The image is classified as **{class_label}**.")
+        
+# if __name__ == "__main__":
+#     main()
+
 import streamlit as st
 import tensorflow as tf
 from tensorflow.keras.preprocessing import image
@@ -5,40 +121,15 @@ import numpy as np
 from PIL import Image
 import base64
 
+# Hide Streamlit menu and footer
 hide_streamlit_style = """
-                <style>
-                div[data-testid="stToolbar"] {
-                visibility: hidden;
-                height: 0%;
-                position: fixed;
-                }
-                div[data-testid="stDecoration"] {
-                visibility: hidden;
-                height: 0%;
-                position: fixed;
-                }
-                div[data-testid="stStatusWidget"] {
-                visibility: hidden;
-                height: 0%;
-                position: fixed;
-                }
-                #MainMenu {
-                visibility: hidden;
-                height: 0%;
-                }
-                header {
-                visibility: hidden;
-                height: 0%;
-                }
-                footer {
-                visibility: hidden;
-                height: 0%;
-                }
-                </style>
-                """
+    <style>
+        #MainMenu {visibility: hidden;}
+        footer {visibility: hidden;}
+    </style>
+    """
 st.markdown(hide_streamlit_style, unsafe_allow_html=True)
 
-
 # Load the pre-trained model
 model = tf.keras.models.load_model('model.h5') 
 # Define the target size for the model
@@ -73,20 +164,62 @@ def display_image_with_download(image_path, caption, download_text):
 
 # Streamlit app
 def main():
-        
-    st.title("Pneumonia Detection")
+    # Set app title and page icon
+    st.set_page_config(
+        page_title="Pneumonia Detection App",
+        page_icon=":microscope:",
+        layout="wide"
+    )
+
+    # Add custom CSS for styling
+    st.markdown("""
+        <style>
+            body {
+                background-color: #f5f5f5;
+            }
+            .st-bw {
+                background-color: #ffffff;
+                box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
+                border-radius: 10px;
+                padding: 20px;
+                margin-bottom: 20px;
+            }
+            .st-bw img {
+                max-width: 100%;
+                border-radius: 10px;
+            }
+            .st-bw a {
+                color: #007bff;
+            }
+            .st-bw button {
+                background-color: #007bff;
+                color: #ffffff;
+                border: none;
+                padding: 10px 20px;
+                font-size: 16px;
+                border-radius: 5px;
+                cursor: pointer;
+            }
+            .st-bw button:hover {
+                background-color: #0056b3;
+            }
+        </style>
+    """, unsafe_allow_html=True)
+
+    # Display app title
+    st.title("Pneumonia Detection App")
 
     # Allow user to upload an image
-    uploaded_file = st.file_uploader("Upload a chest X-ray image in JPG format...", type="jpg")
+    uploaded_file = st.file_uploader("Upload a chest X-ray image in JPG format...", type="jpg", key="fileUploader")
 
     # Example instructions
     st.markdown("""
-        Example Instructions:
+        **Example Instructions:**
         - Upload a chest X-ray image in JPG format.
         - Or, download sample images below and check the predictions.
     """)
 
-   # Provide links to download sample images
+    # Provide links to download sample images
     st.write("**Download Sample Images:**")
     
     pneumonic_download = st.button("Download Pneumonic Image")
@@ -101,7 +234,10 @@ def main():
         display_image_with_download(normal_image_path, "Normal Image", "Normal Image")
 
     if uploaded_file is not None:
+        # Display the uploaded image in a styled container
+        st.markdown('<div class="st-bw">', unsafe_allow_html=True)
         st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
+        st.markdown('</div>', unsafe_allow_html=True)
 
         # Make predictions
         prediction = predict_image(uploaded_file)
@@ -110,6 +246,6 @@ def main():
         st.write("**Prediction:**")
         class_label = "Pneumonia" if prediction > 0.5 else "Normal"
         st.write(f"The image is classified as **{class_label}**.")
-        
+
 if __name__ == "__main__":
     main()